Robust generalized observer design for uncertain one-sided Lipschitz systems

  • Bilal Shaheen
  • , Muhammad Shahid Nazir
  • , Muhammad Rehan*
  • , Sohaira Ahmad
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

This paper develops a novel generalized observer design approach for the uncertain descriptor systems with one-sided Lipschitz nonlinearities, parametric uncertainties, and external perturbations. The nonlinearities, uncertainties, and external perturbations are considered both in input and output equations to consider a matter-of-fact observer design. A generalized structure for the observer is employed to deal with a large number of systems, considering both non-singular and singular systems. The proposed observer scheme is based on non-strict and strict linear matrix inequalities (LMIs), which are derived using the concepts of generalized Lyapunov theory, uncertainty bounds, quadratic inner-boundedness, one-sided Lipschitz condition, matrix transformations, and L2 gain minimization criteria. The proposed state filtering approach is robust for disturbances with asymptotically stable estimation error dynamics under zero external perturbations and attenuation of disturbance effects to keep the estimation error within prescribed limits. In comparison to the conventional observer designs for the one-sided Lipschitz systems, the presented scheme is based on a generalized observer and can deal with parametric uncertainties. A numerical simulation example and an application example of spring-mass-damper system are provided to verify the effectiveness of the suggested filtering schemes.

Original languageEnglish
Article number124588
JournalApplied Mathematics and Computation
Volume365
DOIs
StatePublished - 15 Jan 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Inc.

Keywords

  • Observer design
  • One-sided Lipschitz nonlinearity
  • Quadratic inner-boundedness
  • Singular systems
  • Uncertain systems

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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